The process of measuring functional information such as blood flow within a body of a subject non-invasively is useful in diagnosing and treating the subject. This is particularly the case where a part of a subject or patient, such as a tissue or organ, suffers from diseases due, for example, to cancer or malfunction. Identifying and analysing the characteristics of blood passing through such a tissue or organ can provide important information to medical personnel in order to determine an appropriate treatment regime for the patient.
Existing systems pertaining to the obtaining of blood flow information in vivo have been disclosed. In general, the systems involve a contrast agent that is delivered as an intravascular bolus during a dynamic imaging session such as magnetic resonance imaging (MRI), computerized tomography (CT), nuclear medicine (NM) or positron emission tomography (PET). The temporal profile of the image intensity in a pixel or region of interest (ROI) reflects the characteristics of the contrast agent and hence the blood passing through the tissue.
For example, a typical breast MRI study involves at least one baseline scan before the contrast agent is injected and at least two post-contrast scans after the injection. A commonly used method for diagnostic screening of breast cancer is based on a subjective evaluation of the signal intensity time-curve of a ROI using a visual classification system. FIG. 1 shows a classification system for visual evaluation of five types of enhancement curves of signal intensity as a function of time, where the higher numbered curves are interpreted as representing more aggressive tumor types. The initial sharp rise in plots III, IV and V is due to rapid contrast enhancement. Such a classification scheme was used to achieve very good diagnostic performance in differentiating malignant from benign breast lesions (Daniel, et al. “Breast disease: dynamic spiral MR imaging”, Radiology 209, pp 499-509, 1998).
A breast MRI study usually generates many hundreds of images, it can be very time consuming for medical personnel to review all the images. Methods using computer algorithms have been disclosed in an attempt to automatically detect and characterize lesions into malignant or benign classes with dynamic MRI images (see U.S. Pat. No. 6,353,803 and United States Patent Application Nos 20050074149, 20060018548 and 20060110018). In addition, lesion morphology such as architectural features identified on high spatial resolution images may reflect the underlying pathology and hence may be used to characterize lesions as benign or malignant. Methods combining kinetic and morphologic information in order to provide optimal discrimination between benign and malignant disease have also been disclosed (see U.S. Pat. No. 6,317,617 and United States Patent Application No 20060245629). However, existing methods used for classifying the enhancement curves are based on arbitrary selection of a limited number of time points, and subsequent classification based on calculated descriptive parameters are more sensitive to image noise fluctuations. Further, these methods provide no statistical significance information associated with the classification for the enhancement curve of each pixel.
Although specialist tools providing automated detection and classification of abnormalities can potentially help ease many of the image reviewing challenges, such tools are not widely available, and the reliability of such tools is yet to be validated. Most commonly used tools available from either commercial or academic software for dynamic enhancement analysis include simple subtraction, maximum intensity projection (MIP), multi-planar reconstruction (MPR) and signal intensity time-curve analysis. The time-curve analysis in everyday practice uses the region of interest (ROI) method, where the subtraction images are used to guide the ROI placement over the suspected lesion by the user, while the time-curve of the ROI average signal intensity is presented for visual evaluation and classification.
However, it should be noted that heterogeneity of curve shapes within a lesion is also diagnostically informative. A malignant tumor may well enhance with different types of curve shapes in different anatomical areas, which is a strong indicator of malignancy. The commonly used ROI method in everyday practice is prone to partial volume effects and could not effectively present such heterogeneous information to the user for detailed analysis. It is desirable for a system to provide automated classification of curve patterns with statistical significance information using all available time points of the data, coupled with simultaneous visualization of detailed spatial and temporal information in a real time fashion, which is particularly useful in a busy clinical environment.
The present invention seeks an improved method to substantially overcome or at least ameliorate any one or more of the above-mentioned disadvantages associated with automated classification and visualization of both morphologic and kinetic information in order to assist medical personnel to most efficiently characterize the lesion for optimal discrimination between benign and malignant disease.